
import pandas as pd
import os


os.getcwd()

'C:\\Users\\belwa\\Documents\\Practice'


os.chdir("C:\\Users\\belwa\\OneDrive\\Documents\\0Files\\Learning\\Acmegrade\\Jul 23 DS Day 16\\Projects\\Detection of Parkinsons Disease\\")


df = pd.read_csv("parkinsons.data")
df


df.head() #head returns top 5 rows of data


df.tail() #tail returns bottom 5 rows of data


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 24 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   name              195 non-null    object 
 1   MDVP:Fo(Hz)       195 non-null    float64
 2   MDVP:Fhi(Hz)      195 non-null    float64
 3   MDVP:Flo(Hz)      195 non-null    float64
 4   MDVP:Jitter(%)    195 non-null    float64
 5   MDVP:Jitter(Abs)  195 non-null    float64
 6   MDVP:RAP          195 non-null    float64
 7   MDVP:PPQ          195 non-null    float64
 8   Jitter:DDP        195 non-null    float64
 9   MDVP:Shimmer      195 non-null    float64
 10  MDVP:Shimmer(dB)  195 non-null    float64
 11  Shimmer:APQ3      195 non-null    float64
 12  Shimmer:APQ5      195 non-null    float64
 13  MDVP:APQ          195 non-null    float64
 14  Shimmer:DDA       195 non-null    float64
 15  NHR               195 non-null    float64
 16  HNR               195 non-null    float64
 17  status            195 non-null    int64  
 18  RPDE              195 non-null    float64
 19  DFA               195 non-null    float64
 20  spread1           195 non-null    float64
 21  spread2           195 non-null    float64
 22  D2                195 non-null    float64
 23  PPE               195 non-null    float64
dtypes: float64(22), int64(1), object(1)
memory usage: 36.7+ KB


df.shape

(195, 24)


len(df)

195


df.dtypes

name                 object
MDVP:Fo(Hz)         float64
MDVP:Fhi(Hz)        float64
MDVP:Flo(Hz)        float64
MDVP:Jitter(%)      float64
MDVP:Jitter(Abs)    float64
MDVP:RAP            float64
MDVP:PPQ            float64
Jitter:DDP          float64
MDVP:Shimmer        float64
MDVP:Shimmer(dB)    float64
Shimmer:APQ3        float64
Shimmer:APQ5        float64
MDVP:APQ            float64
Shimmer:DDA         float64
NHR                 float64
HNR                 float64
status                int64
RPDE                float64
DFA                 float64
spread1             float64
spread2             float64
D2                  float64
PPE                 float64
dtype: object


df.describe().to_excel("Summary.xlsx")


# status - health status of the subject (one) - Parkinson's, (zero) – healthy


import matplotlib.pyplot as plt


df.status.hist()
plt.xlabel("status")
plt.ylabel("No. of Patients")
plt.plot()
plt.show()


import pandas as pd
from ydata_profiling import ProfileReport


ProfileReport(df)

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]


plt.figure(figsize=(10,6))
df.status.hist()
plt.xlabel("Status")
plt.ylabel("Frequencies")

Text(0, 0.5, 'Frequencies')


plt.figure(figsize=(10,6))
plt.bar(df.status, df.NHR)
plt.xlabel("STATUS")
plt.ylabel("NHR")

Text(0, 0.5, 'NHR')


import seaborn as sns
plt.figure(figsize=(10,6))
sns.barplot(x = "status", y = "NHR", data = df)
plt.show()


import seaborn as sns
plt.figure(figsize=(10,6))
sns.barplot(x = "status", y = "HNR", data =df)
plt.show()


import seaborn as sns
plt.figure(figsize = (10,6))
sns.barplot(x = "status" , y = "RPDE", data = df)
plt.show()


import warnings
warnings.filterwarnings('ignore')
rows = 3
columns = 7
fig, ax = plt.subplots(nrows = rows , ncols = columns, figsize = (16,7))
col = df.columns
index = 1
for i in range(rows):
    for j in range(columns):
        sns.distplot(df[col[index]], ax = ax[i,j])
        index = index + 1
        
plt.tight_layout()
plt.show()


corr = df.iloc[:,1:].corr()


plt.figure(figsize = (20,10))
sns.heatmap(corr, xticklabels = corr.columns, yticklabels = corr.columns, annot = True)
plt.show()


plt.figure(figsize = (20,10))
sns.heatmap(corr)
plt.show()


#droping the name column
#df = df.iloc[:,1:]
df.drop(['name'], axis = 1, inplace = True)
df


#spliting data into x and y 
x = df.drop(labels=['status'], axis = 1)
display(x.head())
y = df['status']
display(y.head())

0    1
1    1
2    1
3    1
4    1
Name: status, dtype: int64


#spliting data into xtrain, ytrain and xtest, ytest
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2,random_state = 21)


print(x.shape, y.shape)
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)

(195, 22) (195,)
(156, 22) (156,) (39, 22) (39,)


from sklearn.metrics import accuracy_score, confusion_matrix, classification_report


#Create a Logistic regression Model
from sklearn.linear_model import LogisticRegression as logReg
LR = logReg().fit(x_train, y_train)
#predict on train
LR_pred_train = LR.predict(x_train)
#predict on test
LR_pred = LR.predict(x_test)
print("Model accuracy on test is: " , accuracy_score(y_test, LR_pred))
print('-'*50)
#CONFUSION MATRIX
print("confusion_matrix train is \n")
display( confusion_matrix(y_train, LR_pred_train))
print("confusion_matrix test is \n", confusion_matrix(y_test, LR_pred))
print("\n Classification Report Train is")
print(classification_report(y_train, LR_pred_train))
print("\n Classification Report Test is")
print(classification_report(y_test, LR_pred))

Model accuracy on test is:  0.8974358974358975
--------------------------------------------------
confusion_matrix train is

array([[ 21,  18],
       [  6, 111]], dtype=int64)

confusion_matrix test is 
 [[ 6  3]
 [ 1 29]]

 Classification Report Train is
              precision    recall  f1-score   support

           0       0.78      0.54      0.64        39
           1       0.86      0.95      0.90       117

    accuracy                           0.85       156
   macro avg       0.82      0.74      0.77       156
weighted avg       0.84      0.85      0.84       156


 Classification Report Test is
              precision    recall  f1-score   support

           0       0.86      0.67      0.75         9
           1       0.91      0.97      0.94        30

    accuracy                           0.90        39
   macro avg       0.88      0.82      0.84        39
weighted avg       0.89      0.90      0.89        39


y_test.shape

(39,)


#Random Forest
from sklearn.ensemble import RandomForestClassifier
RF = RandomForestClassifier().fit(x_train, y_train)
RF_pred_train = RF.predict(x_train)
RF_pred_test = RF.predict(x_test)
print('Modal accuracy on train is: ', accuracy_score(y_train, RF_pred_train))
print('Modal accuracy on test is: ', accuracy_score(y_test, RF_pred_test))
print('confusion matrix on train \n', confusion_matrix(y_train, RF_pred_train))
print('confusion matrix on test \n', confusion_matrix(y_test, RF_pred_test))
print('Classification matrix on train is \n', classification_report(y_train, RF_pred_train))
print('Classifiaction matrix on test is \n', classification_report(y_test, RF_pred_test))

Modal accuracy on train is:  1.0
Modal accuracy on test is:  0.8717948717948718
confusion matrix on train 
 [[ 39   0]
 [  0 117]]
confusion matrix on test 
 [[ 6  3]
 [ 2 28]]
Classification matrix on train is 
               precision    recall  f1-score   support

           0       1.00      1.00      1.00        39
           1       1.00      1.00      1.00       117

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156

Classifiaction matrix on test is 
               precision    recall  f1-score   support

           0       0.75      0.67      0.71         9
           1       0.90      0.93      0.92        30

    accuracy                           0.87        39
   macro avg       0.83      0.80      0.81        39
weighted avg       0.87      0.87      0.87        39


print((y_test !=RF_pred_test).sum(),'/',((y_test == RF_pred_test).sum()+(y_test != RF_pred_test).sum()))

5 / 39


from sklearn import metrics
print('KappaScore is: ', metrics.cohen_kappa_score(y_test,RF_pred_test))

KappaScore is:  0.6242774566473989


#Decision TREE
from sklearn.tree import DecisionTreeClassifier
DT = DecisionTreeClassifier().fit(x_train, y_train)

#predict on train
DT_pred_train = DT.predict(x_train)

#predict on test
DT_pred_test = DT.predict(x_test)

#accuracy on train and test
print('accuracy on test: ', accuracy_score(y_test, DT_pred_test))
print('accuracy on train: ', accuracy_score(y_train, DT_pred_train))
print('-' * 50)

#Confusion Matrix
print('Confusion Matrix on test: \n', confusion_matrix(y_test, DT_pred_test))
print('Confusion Matrix on train: \n', confusion_matrix(y_train, DT_pred_train))
print('-' * 50)

#Classification_Report
print('Classification Report on test: \n', classification_report(y_test, DT_pred_test))
print('Classification Report on train: \n', classification_report(y_train, DT_pred_train))
print('-'*50)

accuracy on test:  0.8974358974358975
accuracy on train:  1.0
--------------------------------------------------
Confusion Matrix on test: 
 [[ 8  1]
 [ 3 27]]
Confusion Matrix on train: 
 [[ 39   0]
 [  0 117]]
--------------------------------------------------
Classification Report on test: 
               precision    recall  f1-score   support

           0       0.73      0.89      0.80         9
           1       0.96      0.90      0.93        30

    accuracy                           0.90        39
   macro avg       0.85      0.89      0.87        39
weighted avg       0.91      0.90      0.90        39

Classification Report on train: 
               precision    recall  f1-score   support

           0       1.00      1.00      1.00        39
           1       1.00      1.00      1.00       117

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156

--------------------------------------------------


print((y_test!= DT_pred_test).sum(),'/',((y_test==DT_pred_test).sum()+(y_test != DT_pred_test).sum()))
print('-'*50)
print('Kappa Score is:', metrics.cohen_kappa_score(y_test, DT_pred_test))

4 / 39
--------------------------------------------------
Kappa Score is: 0.731958762886598


#Naive bayes
from sklearn.naive_bayes import GaussianNB
NB = GaussianNB().fit(x_train, y_train)

#predict on train
NB_pred_train = NB.predict(x_train)

#predict on test
NB_pred_test = NB.predict(x_test)

#accuracy on train and test
print('accuracy on test: ', accuracy_score(y_test, NB_pred_test))
print('accuracy on train: ', accuracy_score(y_train, NB_pred_train))
print('-' * 50)

#Confusion Matrix
print('Confusion Matrix on test: \n', confusion_matrix(y_test, NB_pred_test))
print('Confusion Matrix on train: \n', confusion_matrix(y_train, NB_pred_train))
print('-' * 50)

#Classification_Report
print('Classification Report on test: \n', classification_report(y_test, NB_pred_test))
print('Classification Report on train: \n', classification_report(y_train, NB_pred_train))
print('-'*50)

accuracy on test:  0.7435897435897436
accuracy on train:  0.7115384615384616
--------------------------------------------------
Confusion Matrix on test: 
 [[ 8  1]
 [ 9 21]]
Confusion Matrix on train: 
 [[37  2]
 [43 74]]
--------------------------------------------------
Classification Report on test: 
               precision    recall  f1-score   support

           0       0.47      0.89      0.62         9
           1       0.95      0.70      0.81        30

    accuracy                           0.74        39
   macro avg       0.71      0.79      0.71        39
weighted avg       0.84      0.74      0.76        39

Classification Report on train: 
               precision    recall  f1-score   support

           0       0.46      0.95      0.62        39
           1       0.97      0.63      0.77       117

    accuracy                           0.71       156
   macro avg       0.72      0.79      0.69       156
weighted avg       0.85      0.71      0.73       156

--------------------------------------------------


print((y_test!= NB_pred_test).sum(),'/',((y_test==NB_pred_test).sum()+(y_test != NB_pred_test).sum()))
print('-'*50)
print('Kappa Score is:', metrics.cohen_kappa_score(y_test, NB_pred_test))

10 / 39
--------------------------------------------------
Kappa Score is: 0.4491525423728814


# K Neighbour Classifier
from sklearn.neighbors import KNeighborsClassifier
KNN = KNeighborsClassifier().fit(x_train, y_train)

#predict on train
KNN_pred_train = KNN.predict(x_train.values)

#predict on test
KNN_pred_test = KNN.predict(x_test.values)

#accuracy on train and test
print('accuracy on test: ', accuracy_score(y_test, KNN_pred_test))
print('accuracy on train: ', accuracy_score(y_train, KNN_pred_train))
print('-' * 50)

#Confusion Matrix
print('Confusion Matrix on test: \n', confusion_matrix(y_test, KNN_pred_test))
print('Confusion Matrix on train: \n', confusion_matrix(y_train, KNN_pred_train))
print('-' * 50)

#Classification_Report
print('Classification Report on test: \n', classification_report(y_test, KNN_pred_test))
print('Classification Report on train: \n', classification_report(y_train, KNN_pred_train))
print('-'*50)

accuracy on test:  0.8205128205128205
accuracy on train:  0.8910256410256411
--------------------------------------------------
Confusion Matrix on test: 
 [[ 4  5]
 [ 2 28]]
Confusion Matrix on train: 
 [[ 28  11]
 [  6 111]]
--------------------------------------------------
Classification Report on test: 
               precision    recall  f1-score   support

           0       0.67      0.44      0.53         9
           1       0.85      0.93      0.89        30

    accuracy                           0.82        39
   macro avg       0.76      0.69      0.71        39
weighted avg       0.81      0.82      0.81        39

Classification Report on train: 
               precision    recall  f1-score   support

           0       0.82      0.72      0.77        39
           1       0.91      0.95      0.93       117

    accuracy                           0.89       156
   macro avg       0.87      0.83      0.85       156
weighted avg       0.89      0.89      0.89       156

--------------------------------------------------


print((y_test!= KNN_pred_test).sum(),'/',((y_test==KNN_pred_test).sum()+(y_test != KNN_pred_test).sum()))
print('-'*50)
print('Kappa Score is:', metrics.cohen_kappa_score(y_test, KNN_pred_test))

7 / 39
--------------------------------------------------
Kappa Score is: 0.4276729559748428


#SVM
from sklearn.svm import SVC
SVM = SVC(kernel = 'linear')
SVM.fit(x_train, y_train)

#predict on train
SVM_pred_train = SVM.predict(x_train)

#predict on test
SVM_pred_test = SVM.predict(x_test)

#accuracy on train and test
print('accuracy on test: ', accuracy_score(y_test, SVM_pred_test))
print('accuracy on train: ', accuracy_score(y_train, SVM_pred_train))
print('-' * 50)

#Confusion Matrix
print('Confusion Matrix on test: \n', confusion_matrix(y_test, SVM_pred_test))
print('Confusion Matrix on train: \n', confusion_matrix(y_train, SVM_pred_train))
print('-' * 50)

#Classification_Report
print('Classification Report on test: \n', classification_report(y_test, SVM_pred_test))
print('Classification Report on train: \n', classification_report(y_train, SVM_pred_train))
print('-'*50)

accuracy on test:  0.8974358974358975
accuracy on train:  0.8717948717948718
--------------------------------------------------
Confusion Matrix on test: 
 [[ 5  4]
 [ 0 30]]
Confusion Matrix on train: 
 [[ 21  18]
 [  2 115]]
--------------------------------------------------
Classification Report on test: 
               precision    recall  f1-score   support

           0       1.00      0.56      0.71         9
           1       0.88      1.00      0.94        30

    accuracy                           0.90        39
   macro avg       0.94      0.78      0.83        39
weighted avg       0.91      0.90      0.89        39

Classification Report on train: 
               precision    recall  f1-score   support

           0       0.91      0.54      0.68        39
           1       0.86      0.98      0.92       117

    accuracy                           0.87       156
   macro avg       0.89      0.76      0.80       156
weighted avg       0.88      0.87      0.86       156

--------------------------------------------------


print((y_test!= SVM_pred_test).sum(),'/',((y_test==SVM_pred_test).sum()+(y_test != SVM_pred_test).sum()))
print('-'*50)
print('Kappa Score is:', metrics.cohen_kappa_score(y_test, SVM_pred_test))

4 / 39
--------------------------------------------------
Kappa Score is: 0.6578947368421053


import pickle 
# Saving model to disk
pickle.dump(SVM,open('deploy_SVM.pkl','wb'))
# Open the Pickle File 
model=pickle.load(open('deploy_SVM.pkl','rb'))
# Prediction 
print (model.predict (x_train))

[1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1
 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1
 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1
 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1
 1 1 1 1 1 1 1 1]

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	phon_R01_S50_2	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	phon_R01_S50_3	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	phon_R01_S50_4	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	phon_R01_S50_5	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	phon_R01_S50_6	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	RPDE	DFA	spread1	spread2	D2	PPE
190	phon_R01_S50_2	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	...	0.07008	0.02764	19.517	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	phon_R01_S50_3	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	...	0.04812	0.01810	19.147	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	phon_R01_S50_4	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	...	0.03804	0.10715	17.883	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	phon_R01_S50_5	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	...	0.03794	0.07223	19.020	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	phon_R01_S50_6	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	...	0.03078	0.04398	21.209	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	0.426	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	0.482	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	0.517	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	0.584	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	0.405	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	0.263	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	0.256	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	0.241	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	0.190	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	MDVP:APQ	Shimmer:DDA	NHR	HNR	RPDE	DFA	spread1	spread2	D2	PPE
0	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	0.426	...	0.02971	0.06545	0.02211	21.033	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.04368	0.09403	0.01929	19.085	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	0.482	...	0.03590	0.08270	0.01309	20.651	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	0.517	...	0.03772	0.08771	0.01353	20.644	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	0.584	...	0.04465	0.10470	0.01767	19.649	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335

Understanding Data¶

WHAT IS PARKINSON'S DISEASE?¶

WHAT IS MDVP?¶